Values in the wild: Discovering and analyzing values in real-world language model interactions

Introduced a practical, large-scale method to observe and analyze the values expressed by Claude in real-world interactions. Developed a privacy-preserving system to categorize and summarize conversations, creating a taxonomy of AI values. Analyzed 308,210 anonymized conversations to provide the first large-scale results on Claude's value expression. Identified prevalent values (Practical, Epistemic, Social, Protective, Personal) and granular values like 'professionalism,' 'clarity,' and 'transparency.' Demonstrated that Claude generally adheres to prosocial values ('user enablement,' 'epistemic humility,' 'patient wellbeing') but also identified rare clusters of 'dominance' and 'amorality' linked to jailbreaks. Showcased Claude's context-dependent value adaptation, such as emphasizing 'healthy boundaries' in relationship advice and 'historical accuracy' in historical analysis. Quantified instances of value mirroring, reframing, and strong resistance to user values. Provided an open dataset for further research and discussed limitations, including the fuzzy nature of value definition and potential biases in the categorization model. Highlighted the method's utility for monitoring AI behavior in the wild and identifying emergent issues like jailbreaks.